Conventional methods for the determination of chemical parameters of the fruit like soluble solids and acid content are often complicated and destructive, cannot be run on a large scale and are still far away from being implemented to large volumes of products or even better to individual piece fruits. In this study, the potential of hyperspectral imaging was evaluated for quantifying solid soluble content (SSC) and titratable acidity (TA) in intact oranges. Hyperspectral images (900–1700 nm) of 264 oranges collected during 2017 and 2018 at different maturation stages in Southern Spain farms were recorded. Partial least-squares analysis (PLS), Artificial Neural Network (ANN), optimized Support Vector Machine (SVM) and Gaussian Process Regression (GPR), as well as different spectral pre-processing methods, were tested for their effectiveness in quantifying titratable acidity (TA) and solid soluble content (SSC) in intact oranges. Random samples were chosen to validate the models by cross-validation. The best-selected models were then applied to a validation set of “unknown” samples and standard errors of prediction as well as correlation coefficients between actual and predicted values were calculated. Finally, a prediction map was developed to display the concentration distribution of the TA and SSC in the orange fruit, demonstrating that hyperspectral imaging (HSI) technique was feasible to quantify parameters in citrus fruit and can be further used for monitoring the quality of oranges at pre- and post-harvest in real-time.
The research published on animal protein by-products (ABPs) has been conducted using at- line instruments. The aim of this study is to evaluate different strategies to transfer a large spectral database of ABPs recorded in a monochromator instrument, to a FT-NIR instrument coupled to a fibre optic probe of 100 metres length, for the on-site quality control. The results obtained demonstrated that, once a large spectral data base of ABPs (more than 1300 samples) has been transferred from the monochromator to the FT-NIR instrument, the calibrations developed for on-site analysis have similar accuracy that those used previously for at-line analysis.
Currently, it is very demanded by nutritionists the availability of real-time on farm analysis for Total Mixed rations ( TMR ) quality control at the level of individual dairy farms. This study refers to the prediction of Crude Protein ( CP) in TMR, after transference of a library file ( N =394 ) of TMR samples from a monochoromator instrument, to two on-farm portable instruments (NIR4Farm, AUNIR, UK and AURORA, GraiNIT, Italy). The results obtained demonstrated that CP can be predicted by NIRS at “ on farm level”, with an accuracy similar to the most expensive at-line laboratory instruments.
Acorn Iberian ham (Jamón Ibérico de Bellota) is one of the most expensive luxury foodstuffs produced in Europe, with a highly appreciated smell and flavour. Its recognized high-sensorial quality and health properties are mainly due to the traditional outdoor feeding system (Montanera) of Iberian pigs (IP), which provides high standards of animal welfare. Nowadays, one of the frauds affecting this product is the use of “special compound feeds” to simulate the fat composition of the acorns through the inclusion of sources of oleic acid like the ones found in pigs fed outdoors. The high prices paid for a cured leg of Iberian ham –ranging from hundreds to thousands of euros- leads to many opportunities for mislabelling and fraud. Fatty acid content of the adipose tissue could provide evidence of the feeding system. Gas chromatography (GC) is used at industry level for production control purposes. However, it is costly and time-consuming, and it is only applied to batches of animals rather than individual pigs. The main goal of this study was to use spectra belonging to a portable NIRS instrument (MicroNIR Onsite Lite, Viavi Solutions Inc.) for on–site quantitative (fatty acid content) analysis of individual Iberian pork carcasses at the slaughterhouse. Performance of this portable instrument was compared with an at-line NIRS monochromator. PLS models were built and optimized resulting in standard errors of cross validation ranging from 0.83 to 0.84 for palmitic acid, 0.94 to 0.99 for stearic acid, 1.47 to 1.56 for oleic acid and 0.53 to 0.58 for linoleic acid.
KEYWORDS: Near infrared spectroscopy, Statistical analysis, Spectral resolution, Spectroscopy, Tissues, Databases, Matrices, Statistical modeling, Communication and information technologies, Satellites
This research is framed within FoodIntegrity, EU sponsored project(7th FP). The main goal of the research to be done is to provide industrials, producers and consumers with a methodology based in low-cost, portable and miniature NIRS sensors and information and communication technologies for process control and voluntary labelling, to guarantee the integrity of the EU high added-value as the “acorn Iberian pig ham”. The present study is focussed in transferring a database (470 samples) of IP tissue - analysed in a FOSS-NIRSystems 6500 (FNS6500) spectrometer, during the seasons 2009-2011 - to a portable/miniature instrument MicroNIR-Onsite, VIAVI (MN1700). A set of 30 samples of adipose tissue was taken from a slaughterhouse during 2015-2016, being analysed in parallel in the satellite (FNS 6500) and master (MN 1700) instruments. Latter on, they were divided in two sets: N = 10 for building the standardization matrices and N = 20 for the validation of the cloning procedure. The algorithm Piece-Wise Direct Standardization (PDS) was applied. The best standardisation matrix was applied to the library of 470 samples taken in the FNS 6500, enabling an excellent fitting between both instruments, as shown the RMSCs statistic calculated in the satellite before and after the standardization and in the master - 108457 vs 22519 vs 17646 μlog 1/R – and the GH distance before and after standardisation between both instruments 437.41 vs 2.06.
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